Topic

Remarks

Week 1

Resource Person

Python – Basics of Programming (data structures, control structures, array, matrix, OO Programming, Exception Handling

Feature Engineering –features extraction from numeric, categorical, feature scaling, feature selection, dimensionality reduction

Dr. V. Pathak

Week 2

Mathematics for ML – Vector spaces: Basis, Vectors dimensions, nullity, Orthogonality of vectors rank projections, Eigenvalue, Eigenvector, decomposition, PCA , Graph Analysis

Maths Faculty

Basics of Statistical Methods in ML – mean, median, mode, Std. dev, correlation, chi-square

Week 3

Basics of R language, data types, data structures, control structures, data frame, vectors, list, factors etc., Data visualization using R, Descriptive Data Analysis using R, Inferential Data Analysis using R

Dr. Seema Gaur

Week 4

Supervised learning - Regression – Linear, Logistic, accuracy measures

Supervised learning - Classification – Decision Tree, kNN, ANN, accuracy measures

Unsupervised learning - Clustering using K-Means, and Hierarchical Methods

Dr. S. Vijayvargiya

Week 5

Fundamentals of Image processing using Python

Mr. Subhash C. Gupta

Week 6

Basics of Perl Programming, Statements, and Operators, Data Types, Scalar, Array, HashPerl Loops, Logical and Conditional expressions, Perl Strings, String Manipulations, String Functions, Subroutines and File Handling, Perl References, Regular Expression, Perl Modules and CGI Applications

Dr. P. S. Solanki

Sno.

Duration

Coverage

1

Week 1

Training Introduction, Process Models, Software Requirement Specifications

2

Week 2

Structured Chart, Unified Modelling Language (UML)

3

Week 3

Black Box Testing, White Box Testing

4

Week 4

ISTQB Certification Preparation, Case Studies, Rational Rose